In this paper we learn the skills required by real-time physics-based
avatars to perform parkour-style fast terrain crossing using a mix
of running, jumping, speed-vaulting, and drop-rolling. We begin
with a single motion capture example of each skill and then learn
reduced-order linear feedback control laws that provide robust execution
of the motions during forward dynamic simulation. We
then parameterize each skill with respect to the environment, such
as the height of obstacles, or with respect to the task parameters,
such as running speed and direction. We employ a continuation
process to achieve the required parameterization of the motions
and their affine feedback laws. The continuation method uses a
predictor-corrector method based on radial basis functions. Lastly,
we build control laws specific to the sequential composition of different skills,
so that the simulated character can robustly transition
to obstacle clearing maneuvers from running whenever obstacles
are encountered. The learned transition skills work in tandem with
a simple online step-based planning algorithm, and together they
robustly guide the character to achieve a state that is well-suited for
the chosen obstacle-clearing motion.